Tree-Based Ensemble Methods and Their Applications for Predicting Students’ Academic Performance

Abstract

Students’ academic performance is a key aspect of online learning success. Online learning applications known as Learning Management Systems (LMS) store various online learning activities. In this research, students’ academic performances in online course X are predicted such that teachers could identify students who are at risk much sooner. The prediction uses tree-based ensemble methods such as Random Forest, XGBoost (Extreme Gradient Boosting), and LightGBM (Light Gradient Boosting Machine). Random Forest is a bagging method, whereas XGBoost and LightGBM are boosting methods. The data recorded in LMS UI, or EMAS (e-Learning Management Systems) is collected. The data consists of activity data for 232 students (219 passed, 13 failed) in course X. This data is divided into three proportions (80:20, 70:30, and 60:40) and three periods (the first, first two, and first three months of the study period). Data is pre-processed using the SMOTE method to handle imbalanced data and implemented in all categories, with and without feature selection. The prediction results are compared to determine the best time for predicting students’ academic performance and how well each model can predict the number of unsuccessful students. The implementation results show that students’ academic performance can be predicted at the end of the second month, with best prediction rates of 86.8%, 80%, and 75% for the LightGBM, Random Forest, and XGBoost models, respectively, with feature selection. Therefore, with this prediction, students who could fail still have time to improve their academic performance